In recent years, the demand for deploying machine learning (ML) in wireless networks and Internet of Things (IoT) applications has increased dramatically. However, due to growing concerns associated with data privacy, it is infeasible to transmit all collected data from IoT edge devices to a central location (e.g., a datacenter) for model training. Federated learning (FL) has emerged as a popular approach for distributed ML which allows for model training without requiring data sharing [1]}, [2]}. Under FL, devices function as workers, training local ML models on their own datasets, and exchange their model updates with an aggregator, e.g., an edge server, over multiple communication rounds to converge on a global model. While FL distributes the data processing step across devices, the model aggregation step is still often carried out at a single location, which imposes security issues including single-point-of-failure and server malfunction. Additionally, this poses scalability restrictions for ML training processes, especially as the number of IoT devices involved and their geographic reach continue to expand [3]}. Therefore, it is desirable to develop a more decentralized FL architecture for realizing scalable model training while preserving security in next-generation intelligent networks.
